计算机科学 ›› 2019, Vol. 46 ›› Issue (11A): 224-229.
马闻锴, 李贵, 李征宇, 韩子扬, 曹科研
MA Wen-kai, LI Gui, LI Zheng-yu, HAN Zi-yang, CAO Ke-yan
摘要: 随着Web2.0的发展,UGC标签系统受到越来越多的关注,标签既能反映用户的兴趣又能描述物品的本身特征。现有的标签推荐算法没有考虑用户的连续行为所产生的影响,而传统的基于马尔可夫链(Markov Chain)的推荐算法虽然侧重于研究用户的连续行为来产生推荐,但它是直接作用于用户与物品的二维关系,并不适用于基于UGC的标签推荐。因此,通过结合马尔可夫链和协同过滤的思想,提出了一种基于标签的个性化推荐算法。该算法将〈用户-标签-物品〉的三维关系拆分为〈用户-标签〉和〈标签-物品〉两个二维关系。首先通过马尔可夫链模型计算用户对标签的兴趣度,再通过推荐标签集来匹配与其相对应的物品。为了提高推荐的精准率,该算法利用标签之间的影响,并基于匹配物品中所含标签间存在的关联关系对物品进行满意度建模,该模型是一种概率模型。在计算用户-标签和用户-物品之间的兴趣度和满意度时使用了协同过滤的思想来补全稀疏值。在公开的数据集中,与现有算法相比,该算法在精准率、召回率上均有明显提高。
中图分类号:
[1]CHEN W,HSU W,LEE M L.A unified framework for recommendations based on quaternary semantic analysis[C]∥International ACM SIGIR Conference on Research and Development in Information Retrieval.ACM,2011:1023-1032. [2]项亮,陈义,王益.推荐系统实践[M].河北:人民邮电出版社,2012:39-43. [3]RENDLE S,FREUDENTHALER C,SCHMIDTTHIEME L.Factorizing personalized Markov chains for next-basket recommendation[C]∥International Conference on World Wide Web.ACM,2010. [4]文俊浩,袁培雷,曾骏,等.基于标签主题的协同过滤推荐算法研究[J].计算机工程,2017(1). [5]SHANI G,HECKERMAN D,BRAFMAN R I.An MDP-Based Recommender System[J].Journal of Machine Learning Research,2005,6(1):1265-1295. [6]BRIN S,PAGE L.The anatomy of a large-scale hypertextualWeb search engine[C]∥International Conference on World Wide Web.Elsevier Science Publishers B.V.,1998:107-117. [7]HOTHO A,JÄSCHKE R,SCHMITZ C,et al.Information Retrieval in Folksonomies:Search and Ranking[J].Semantic Web Research & Applications,2006,4011:411-426. [8]PHAM T A N,LI X,CONG G.A General Model for Out-of-town Region Recommendation[C]∥International Conference.2017. [9]PIRASTEH P,JUNG J J,HWANG D.Item-Based Collaborative Filtering with Attribute Correlation:A Case Study on Movie Recommendation[M]∥Intelligent Information and Database Systems.Springer International Publishing,2014. [10]刘健,张琨,陈旋.基于标签和协同过滤的个性化推荐算法[J].计算机与现代化,2016(2):62-65. [11]蔡强,韩东梅,李海生,等.基于标签和协同过滤的个性化资源推荐[J].计算机科学,2014,41(1):69-71. [12]YE M,YIN P F,LEE W C,et al.Exploiting Geographical In-fluence for Collaborative Point-of-interest Recommendation[C]∥International AcmSigir Conference on Research & Development in Information Retrieval.ACM,2011. [13]李贵,王爽,李征宇,等.基于张量分解的个性化标签推荐算法[J].计算机科学,2015,42(2):267-273. [14]李贵,陈召新,韩子扬,等.基于谱聚类群组发现和马尔可夫链的个性化推荐算法[J].计算机科学,2014,40(10):44-48. [15]李贵,吴炎,孙平,等.基于个性化马尔可夫链的推荐算法[J].计算机科学,2013,40(10):319-322. [16]陈洁敏,李建国,汤非易,等.融合“用户-项目-用户兴趣标签图”的协同好友推荐算法[J].计算机科学与探索,2018. |
[1] | 程章桃, 钟婷, 张晟铭, 周帆. 基于图学习的推荐系统研究综述 Survey of Recommender Systems Based on Graph Learning 计算机科学, 2022, 49(9): 1-13. https://doi.org/10.11896/jsjkx.210900072 |
[2] | 王冠宇, 钟婷, 冯宇, 周帆. 基于矢量量化编码的协同过滤推荐方法 Collaborative Filtering Recommendation Method Based on Vector Quantization Coding 计算机科学, 2022, 49(9): 48-54. https://doi.org/10.11896/jsjkx.210700109 |
[3] | 郑文萍, 刘美麟, 杨贵. 一种基于节点稳定性和邻域相似性的社区发现算法 Community Detection Algorithm Based on Node Stability and Neighbor Similarity 计算机科学, 2022, 49(9): 83-91. https://doi.org/10.11896/jsjkx.220400146 |
[4] | 秦琪琦, 张月琴, 王润泽, 张泽华. 基于知识图谱的层次粒化推荐方法 Hierarchical Granulation Recommendation Method Based on Knowledge Graph 计算机科学, 2022, 49(8): 64-69. https://doi.org/10.11896/jsjkx.210600111 |
[5] | 方义秋, 张震坤, 葛君伟. 基于自注意力机制和迁移学习的跨领域推荐算法 Cross-domain Recommendation Algorithm Based on Self-attention Mechanism and Transfer Learning 计算机科学, 2022, 49(8): 70-77. https://doi.org/10.11896/jsjkx.210600011 |
[6] | 刘冬梅, 徐洋, 吴泽彬, 刘倩, 宋斌, 韦志辉. 基于边框距离度量的增量目标检测方法 Incremental Object Detection Method Based on Border Distance Measurement 计算机科学, 2022, 49(8): 136-142. https://doi.org/10.11896/jsjkx.220100132 |
[7] | 武红鑫, 韩萌, 陈志强, 张喜龙, 李慕航. 监督和半监督学习下的多标签分类综述 Survey of Multi-label Classification Based on Supervised and Semi-supervised Learning 计算机科学, 2022, 49(8): 12-25. https://doi.org/10.11896/jsjkx.210700111 |
[8] | 帅剑波, 王金策, 黄飞虎, 彭舰. 基于神经架构搜索的点击率预测模型 Click-Through Rate Prediction Model Based on Neural Architecture Search 计算机科学, 2022, 49(7): 10-17. https://doi.org/10.11896/jsjkx.210600009 |
[9] | 齐秀秀, 王佳昊, 李文雄, 周帆. 基于概率元学习的矩阵补全预测融合算法 Fusion Algorithm for Matrix Completion Prediction Based on Probabilistic Meta-learning 计算机科学, 2022, 49(7): 18-24. https://doi.org/10.11896/jsjkx.210600126 |
[10] | 蔡晓娟, 谭文安. 一种改进的融合相似度和信任度的协同过滤算法 Improved Collaborative Filtering Algorithm Combining Similarity and Trust 计算机科学, 2022, 49(6A): 238-241. https://doi.org/10.11896/jsjkx.210400088 |
[11] | 何亦琛, 毛宜军, 谢贤芬, 古万荣. 基于点割集图分割的矩阵变换与分解的推荐算法 Matrix Transformation and Factorization Based on Graph Partitioning by Vertex Separator for Recommendation 计算机科学, 2022, 49(6A): 272-279. https://doi.org/10.11896/jsjkx.210600159 |
[12] | 何茜, 贺可太, 王金山, 林绅文, 杨菁林, 冯玉超. 比特币实体交易模式分析 Analysis of Bitcoin Entity Transaction Patterns 计算机科学, 2022, 49(6A): 502-507. https://doi.org/10.11896/jsjkx.210600178 |
[13] | 郭亮, 杨兴耀, 于炯, 韩晨, 黄仲浩. 基于注意力机制和门控网络相结合的混合推荐系统 Hybrid Recommender System Based on Attention Mechanisms and Gating Network 计算机科学, 2022, 49(6): 158-164. https://doi.org/10.11896/jsjkx.210500013 |
[14] | 熊中敏, 舒贵文, 郭怀宇. 融合用户偏好的图神经网络推荐模型 Graph Neural Network Recommendation Model Integrating User Preferences 计算机科学, 2022, 49(6): 165-171. https://doi.org/10.11896/jsjkx.210400276 |
[15] | 朱旭东, 熊贇. 基于样本分布损失的图像多标签分类研究 Study on Multi-label Image Classification Based on Sample Distribution Loss 计算机科学, 2022, 49(6): 210-216. https://doi.org/10.11896/jsjkx.210300267 |
|